Opportunities with Predictive Analytics. Greg Leflar, Vice President

Transcription

1 Opportunities with Predictive Analytics Greg Leflar, Vice President

2 Opportunities for Predictive Analytics

3 We help you separate the Value from the Hype The field of predictive analytics is not yet well understood, but understanding the buzzwords and their differences will help unlock the power of this new set of capabilities We recommend starting small with focused use cases to pilot and taking frequent measurements of value; there is danger in build it and they will come 3 Pariveda Solutions. Confidential & Proprietary.

4 Descriptive vs. Predictive Analytics Prediction What Might Happen? Monitoring What s Happening Now? Analysis Why Did It Happen? Reporting What Happened? Predictive Analytics Descriptive Analytics / Business Intelligence The goal of predictive analytics is to create a competitive advantage by using data to better understand the most likely next action 4 Pariveda Solutions. Confidential & Proprietary.

5 Go beyond Forecasting to Predictive Analytics Forecasting Using trend analysis to forecast the behavior of a large group of people in aggregate Predictive Analytics Attempting to predict individual behavior in order to understand and even influence the future 5 Pariveda Solutions. Confidential & Proprietary.

6 We effectively create optimum Predictive Models The primary purpose of predictive analytics is to use a variety of data analysis techniques to create predictive models A predictive model is a decision tree that uses the correlations and patterns revealed by predictive analytics to provide valuable insights and likelihoods of a particular behavior A predictive model in marketing, for example, may use a customer's gender, age, and purchase history to predict the likelihood of a future sale Data 6 Pariveda Solutions. Confidential & Proprietary. Predictive Analytics Predictive Model

7 Though not always a necessity, we help you increase Predictive accuracy with Big Data While predictive analytics has no dependency on big data, the two are often used together Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate Predictive analytics models can increase their accuracy and effectiveness with large data sets, but large data sets (big data) are not a requirement Because of this relationship, these two terms often become intertwined Accuracy 7 Pariveda Solutions. Confidential & Proprietary. Data Set Size

8 Our experience with Hadoop solutions helps you scale faster and lower costs Enterprise Data Warehouse EDW s, like SQL, have a costly (both monetary and performance) scaling curve when dealing with large data volumes because they are built with a centralized (scale-up) data access and processing model Big Data Solution Big Data solutions, like Hadoop, are built on a distributed data access and processing model (scale-out) that allows for scaling of processing power and storage capacity by adding more reasonably-priced (commodity) servers Cost Overhead of starting up distributed processing causes EDW to perform better with small data sets When data sets become large or calculations complex, Big Data solutions outperform EDW (centralized) Big Data (distributed) Scale 8 Pariveda Solutions. Confidential & Proprietary.

9 We utilize sophisticated technologies like Machine Learning to test your Predictive models Machine learning is another term that is often associated or used synonymously with predictive analytics The term machine learning refers to computer systems that attempt to predict the future using traditional predictive analytics techniques while also capturing feedback and adapting the process in real-time This emerging technology represents one way, and possibly the most sophisticated way, to perform predictive analytics because it gives computers the ability to learn without being explicitly programmed Create a Model Train the Model Score and Test the Model 9 Pariveda Solutions. Confidential & Proprietary.

10 Opportunities for Predictive Analytics

11 Types of Opportunities for Predictive Analytics Customer Acquisition Identifying which customers you want to acquire and determining the most cost-effective ways to approach them Customer Retention Customer Engagement Providing energy management capabilities to customers that add value, build trust, and make them want to remain a customer Operational Efficiency Opportunities for Predictive Identifying which customers are most likely to defect and determining the most costeffective ways to keep them Improve operational efficiencies to offer competitive product pricing, streamlined customer interactions, and improved profitability 11 Pariveda Solutions. Confidential & Proprietary.

12 Let s talk about improving your future with our guidance on Predictive Analytics Greg Leflar, Vice President greg.leflar@parivedasolutions.com